Cross-Domain Coupling Network With Lightweight Fully Featured Mapping and Loop Aggregation for Semantic Segmentation of High-Resolution Remote Sensing Images

To fully leverage contextual information for the precise segmentation of objects in remote sensing images, while addressing the challenges associated with substantial object scale variations and complex backgrounds, we propose a lightweight cross-domain coupling network (LCCN) tailored for the semantic segmentation of high-resolution remote sensing images (HRSIs). To standardize feature selection and fusion procedures, the LCCN incorporates an innovative encoder–coupler–decoder architecture designed to facilitate key feature extraction and optimization. A cross-domain coupling module (CDCM) is created in the coupler to conduct preliminary feature screening of spaces and dimensions based on channel and spatial attention. It performs multiscale feature extraction and global information modeling through feature grouping and loop aggregation. This helps to extract key features while reducing the computational overhead. To further decrease the interference from complex backgrounds, a secondary optimization of the key features is carried out: a lightweight fully featured mapping attention module (LFMAM) is designed within the decoder. LFMAM utilizes an interactive fusion strategy and a lightweight linear self-attention (SA) mechanism, comprehensively considering all interactions between global-to-global, global-to-local, local-to-local, and local-to-global processes. By capturing the effective correlations and variances among features to further refine them, it enables the network to further optimize the crucial information while ensuring lightweight. We have conducted extensive comparison experiments and ablation experiments on the ISPRS Vaihingen and ISPRS Potsdam datasets. The extensive experimental results demonstrate that our proposed LCCN can obtain superior performance compared to other advanced semantic segmentation models.